As the Internet becomes integrated into almost every aspect of people's lives, the amount of content users provide is growing at an exponential rate. It is common for web providers to operate databases with petabytes of data, while leading content providers are already looking toward technology to handle exabyte implementations. One social networking website, for example, has attracted over a billion users who have posted over 250 billion photos, with another 350 million photos being posted each day.
Many of these images are viewable by users other than the image posters, such as the posters friends, members of affiliated groups, or the general public. Some of these images may contain content that some users may find objectionable, such as sexual content, violent scenes, drug use, etc. However, due to the sheer volume of images, it is infeasible for each image to have human review prior to being available to other users. Furthermore, what is considered “objectionable” can vary, such as by location or user age. Thus, images can require multiple levels of classifications such as “objectionable to users under age 18,” “objectionable to users who have been identified as not wanting to see violent images,” or “objectionable to users who are associated with religion X.”
Several computational approaches have been attempted to identify images as containing objectionable content. For example, neural networks have been used to analyze images and classify them as containing sexual content or not. As another example, object recognition techniques have been applied to identify objects in images that may be found objectionable. However, these methods of identifying objectionable content are prone to accuracy errors.
The techniques introduced herein may be better understood by referring to the following Detailed Description in conjunction with the accompanying drawings, in which like reference numerals indicate identical or functionally similar elements.